Details
| Original language | English |
|---|---|
| Pages (from-to) | SWC31-SWC37 |
| Journal | Water resources research |
| Volume | 39 |
| Issue number | 5 |
| Early online date | 17 May 2003 |
| Publication status | Published - May 2003 |
| Externally published | Yes |
Abstract
The discharge of the Rhine River is modeled by using flexible seasonal long-memory models. The memory parameters are estimated by log periodogram regression for every seasonal frequency separately. It turns out that these models fit well the long-term behavior of the river. Significant long-range dependence was estimated at annual and semiannual frequencies. These results are robust against elimination of possible deterministic seasonal structures.
Keywords
- Log periodogram regression, Long memory, Rhine River, Seasonal models
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
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In: Water resources research, Vol. 39, No. 5, 05.2003, p. SWC31-SWC37.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Modeling water flow of the Rhine River using seasonal long memory
AU - Lohre, Michael
AU - Sibbertsen, Philipp
AU - Könning, Tamara
PY - 2003/5
Y1 - 2003/5
N2 - The discharge of the Rhine River is modeled by using flexible seasonal long-memory models. The memory parameters are estimated by log periodogram regression for every seasonal frequency separately. It turns out that these models fit well the long-term behavior of the river. Significant long-range dependence was estimated at annual and semiannual frequencies. These results are robust against elimination of possible deterministic seasonal structures.
AB - The discharge of the Rhine River is modeled by using flexible seasonal long-memory models. The memory parameters are estimated by log periodogram regression for every seasonal frequency separately. It turns out that these models fit well the long-term behavior of the river. Significant long-range dependence was estimated at annual and semiannual frequencies. These results are robust against elimination of possible deterministic seasonal structures.
KW - Log periodogram regression
KW - Long memory
KW - Rhine River
KW - Seasonal models
UR - http://www.scopus.com/inward/record.url?scp=1542646199&partnerID=8YFLogxK
U2 - 10.1029/2002WR001697
DO - 10.1029/2002WR001697
M3 - Article
AN - SCOPUS:1542646199
VL - 39
SP - SWC31-SWC37
JO - Water resources research
JF - Water resources research
SN - 0043-1397
IS - 5
ER -